Abstract

For practical use, the relevance of indoor surveillance from multiple cameras to track the movement of people and reidentify them in video sequences is constantly increasing. This is a complex task due to the effect of uneven illumination, background inhomogeneity, overlap, uncertainty of the trajectories of people, and the similarity of their visual features. The article presents an approach to track people by video sequences and reidentify them in multicamera video surveillance systems that are used indoors. At the first step, people are detected using a YOLO v4 convolution neural network (CNN) and described by a rectangular area. Further, the search for the face area and the calculation of its features are carried out, which in the developed method are used when accompanying a person in a video sequence and during his intercamera reidentification. This approach improves the accuracy of tracking with a complex movement trajectory and multiple intersections of people with similar characteristics. The search for faces is carried out on the detected areas based on the multitasking MTCNN, and the MobileFaceNetwork model is used to form the vector of the features of the face. Human features are generated using a modified CNN based on ResNet34 and an HSV color tone channel histogram. The correspondence between people on different frames is established based on the analysis of the spatial coordinates of faces and people, as well as their CNN features, using the Hungarian algorithm. To ensure the accuracy of intercamera tracking, reidentification is performed based on the facial features. Five test video sequences of different numbers of people captured indoors with a fixed video camera were used to test and compare different approaches. The obtained experimental results confirmed the strength of the characteristics of the proposed approach.

中文翻译：

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多摄像机室内视频监控系统的人员跟踪和身份识别

摘要

在实际应用中，通过多台摄像机进行室内监视以跟踪人员的移动并在视频序列中对其进行识别的重要性不断提高。由于照明不均匀，背景不均匀，重叠，人的轨迹不确定以及其视觉特征的相似性，这是一项复杂的任务。本文提出了一种通过视频序列跟踪人员并在室内使用的多摄像机视频监视系统中对其进行重新识别的方法。第一步，使用YOLO v4卷积神经网络（CNN）对人进行检测，并通过矩形区域进行描述。此外，还进行了面部区域的搜索及其特征的计算，在视频序列中陪同一个人并在摄像机间进行重新识别时，使用已开发方法中的方法。这种方法通过复杂的运动轨迹和具有相似特征的多个交叉路口来提高跟踪的准确性。基于多任务MTCNN，在检测到的区域上进行人脸搜索，并使用MobileFaceNetwork模型形成人脸特征的向量。使用基于ResNet34和HSV色调通道直方图的改进的CNN生成人的特征。使用匈牙利算法，通过分析面部和人物的空间坐标及其CNN特征，建立不同帧上人物之间的对应关系。为了确保摄像机间跟踪的准确性，基于面部特征进行重新识别。使用固定摄像机在室内捕获的五个不同人数的测试视频序列用于测试和比较不同的方法。获得的实验结果证实了所提出方法的强度。